Author-level metrics: <i>h</i>-index and beyond

The main goal of this work is to verify the existence of diverse behaviour patterns in academic production and impact, both among members of the same scientific community (inter-author variability) and for a single author (intra-author variability), as well as to find out whether this fact affects the correlation among author-level metrics in disciplinary studies. To do this, two samples are examined: a general sample (members of a discipline, in this case Bibliometrics; n= 315 authors), and a specific sample (only one author; n= 119 publications). Four author-level metrics (Total Citations, Recent Citations, Reads, and Online mentions) were extracted from three platforms (Google Scholar Citations, ResearchGate, and ImpactStory). The analysis of the general sample reveals the existence of different performance patterns, in the sense that there are groups of authors that perform prominently in some platforms, but exhibit a low impact in the others. The case study shows that the high performance in certain metrics and platforms is due to the coverage of document typologies, which is different in each platform (for example, Reads in working papers). It is concluded that the identification of the behaviour pattern of each author (both at the inter-author and intra-author levels) is necessary to increase the precision and usefulness of disciplinary analyses that use author-level metrics, and thus avoid masking effects

As academic journals have become more digital, scientific writers now have more chances to increase the visibility of their research and its citations globally. The metrics is the gold standard. It is used to decide the ranking and benchmark of the journals which will be determined using bibliometrics and different algorithms. Numerous predatory journals are lack of international standards and low quality. Authors will be unable to submit their research papers or reports to the journals for publication with a high impact factor in this circumstance. The main objective of this study is to focus on identifying the global citations by investigating the author level metrics of the top hundred scholarly publications. Data were gathered from Google Scholar source between January 2021 and September 2022 for the study, which was quantitative in nature. Hypothesis was tested by simulation growth model and statistically approximates the h5 citation and Journal impact factor. Asper the results, top hundred first ranking was seen in Nature Reviews and Molecular Cell Biology (h5 155, IF 113.90 h5 median 340) followed by nature reviews immunology (h5 152, IF 108.60 h5 median 292), The highest ranking discipline that was substantially correlated with citation was health and medical sciences (r = 0.91, R2 % = 0.97) followed by physics and mathematics (r = 0.89, R2 % = 0.94).Finally, this study implies that open access journals should have display metrics information for the researchers which can be act as formidable tool for the publishers, scientists and researchers enabling them to make informed decisions at the appropriate moment and disseminate scientific knowledge globally.

The main goal of this article is to describe the purpose and content of a new branch of bibliometrics: ALMetrics (Author- Level Metrics). ALMetrics is focused on the quantitative analysis of an author’s performance by measuring the dimensions of their intellectual activity as shown through varied metric indicators. This article will list, define, and classify the different metrics that are offered in newer information portals that showcase the scientific activity of authors. These metrics are grouped into five sets: bibliometrics (publication and citation), usage, participation, rating, social connectivity, and composite indicators. This new bibliometric specialty is necessary because of new trends in scientific assessment, which have moved analysis away from old bibliometrics (based on journal analysis and Impact Factor) towards new bibliometrics that analyze both documents and authors via a mix of indicators. Most importantly, ALMetrics responds to the researchers’ desire for both knowledge and acknowledgement.

Associations between author-level metrics in subsequent time periods

RelRank: A relevance-based author ranking algorithm for individual publication venues

Aim. To assess the influence of sex, age and length of service on scientific productivity (h-index in the RSCI, Scopus and Web of Science) of researchers of the National Medical Research Center for Therapy and Preventive Medicine.Material and methods. The study sample was formed from the staff of the National Medical Research Center for Therapy and Preventive Medicine and consisted of scientists of various ranks. The study included 147 people (women, 103; men, 44); Information was collected on their age, education, length of service, academic degree, the position held and author-level metrics — the h-index, obtained from three abstract and citation databases — RSCI, Scopus and Web of Science. All study participants were divided into groups by sex and age categories — young (≤39 years), middle-aged (men, 40-60 years; women, 40-55 years) and older (men >60 years old; women >55 years).Results. Analysis showed that 70,1% of the research team consists of women; 60% — young and middle-aged scientists, and 40% — older people. Among male researchers, compared with women, there are more doctors of science and high h-index values in the RSCI, Scopus and Web of Science. H-index value in the RSCI among male researchers is 47,3% due to the age and work duration at the National Medical Research Center for Therapy and Preventive Medicine, and among female researchers, 42,8% due to academic degree and length of service. The h-index in Scopus in men is influenced by age, in women — by the presence of an academic degree, which explain, respectively, 19,7 and 18,1% of its variability. H-index value in the Web of Science in men is associated with the work duration, while in women, with an academic degree and length of service, which explain, respectively, 24,4 and 21,1% of its variance.Conclusion. Women and young and middle-aged scientists prevail in research team pattern of the National Medical Research Center for Therapy and Preventive Medicine. However, male researchers make a more significant contribution to scientific productivity, assessed by the h-index in the RSCI, Scopus and Web of Science. Age and length of service have the greatest influence on scientific productivity for male researchers, while for women — presence of academic degree and length of service. It is necessary to continue research on the study of individual, motivational and institutional factors affecting the scientific productivity.

The present paper proposes the idea of formulating a new author-level citation metrics, quantifying the individual’s scientific output, which uses the concept of intuitionistic fuzzy sets. This new metrics gives more visibility to the proportion of direct self-citations and hidden (co-author or collaborative) self-citations in the form of an intuitionistic fuzzy pair. Examples are given retrieving the necessary information from Scopus, one of the largest databases of peer-reviewed literature, which algorithmically enables retrieval of one’s citations including and excluding their own or their co-authors’ self-citations, as well as the calculation of three different values of one’s h-index with and without these respectively. Linear and triangular graphical representations are given, as well as comparisons with the concept proposed in 2005 by Jorge E. Hirsch, the now famous h-index, and ideas for future elaboration of the concept of the new intuitionistic fuzzy sets based author-level metrics, or shortly IFALM.

Chapter 103 - Promoting research

In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding. The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.

The purpose was to examine author-level impact metrics for faculty in the communication sciences and disorder research field across a variety of databases. Author-level impact metrics were collected for faculty from 257 accredited universities in the United States and Canada. Three databases (i.e., Google Scholar, ResearchGate, and Scopus) were utilized. Faculty expertise was in audiology (24.4%; n = 490) and speech-language pathology (75.6%; n = 1,520). Women comprised 68.1% of faculty, and men comprised 31.9% of faculty. The percentage of faculty in the field of communication sciences and disorders identified in each database was 10.5% (n = 212), 44.0% (n = 885), and 84.4% (n = 1,696) for Google Scholar, ResearchGate, and Scopus, respectively. In general, author-level impact metrics were positively skewed. Metric values increased significantly with increasing academic rank (p < .05), were greater for men versus women (p < .05), and were greater for those in audiology versus speech-language pathology (p < .05). There were statistically significant positive correlations between all author-level metrics (p < .01). These author-level metrics may serve as a benchmark for scholarly production of those in the field of communication sciences and disorders and may assist with professional identity management, tenure and promotion review, grant applications, and employment.

The present paper makes a systematic review of 131 articles on research productivity basing upon a model developed by Denyer and Tranfield. The current review paper analyzed the quantitative and qualitative dimensions of the sample literature in the field of research productivity. The quantitative analysis incorporates the chronological distribution of articles, keyword wise distribution, form wise distribution, discipline wise distribution, publisher wise distribution, data sources used in the articles, most prolific journals of the publications and qualitative dimensions studied are top-cited articles and all the metrices used in these articles. The paper is based upon literature retrieved from Scopus bibliographic database and other online databases like Emerald, Taylor &amp; Francis, Google Scholar, JSTOR, ProQuest, and EBSCO for full-text articles. It was revealed that there is significant growth in the number of articles during the fourth decade (2011–2020) whereas the highest numbers of citations (1530) have been received during the third decade from 2001 to 2010. Journal articles appeared to be the predominant source of information. A wide range of metrices found to be used in the sample literature to analyze the research productivity namely basic metrics, metrics dealing with growth, collaboration and metrics at institute level and author level metrics. It will serve as a reference tool to the scholars and practitioners in the field who will be acquainted with the publications on research productivity as well as various metrices, laws of bibliometrics, statistical tools, used in the articles.

A commentary on “author level metrics and academic productivity” (int J surg 2021; 90:105,965)

Author level metrics and academic productivity

Ancestry and genealogy tree are proven tools to determine the lineage of any person and establish dependencies among individuals. Genealogy tree can be exploited further to gain information about the researcher and his scholastic lineage which is of paramount importance in today's world of computer technology. this insight into academic genealogy could be a way of helping PHD students achieve academic socialization within the discipline, by making explicit connections that may be influential. Awareness of his scientific heritage gives the user a broader perspective of his own research project. This paper also highlights and investigates how this academic network is exploited by certain researchers using various visualization tools. It was observed during this work that the credibility and influence factor is determined by the various citations obtained by an author and to improve their rankings in various forms, they tend to collaborate in their academic circle and boost their citation count. A recent trend among researchers is to form communities based on their academic relationships and rely on copious citations for their mutual benefit. Tracing the genealogical relationships can be helpful in detecting such communities and also create a more quality aware metric using a lineage independent model for computation of author level metrics.

An author level metrics of scholarly impact journals; cited through Google Scholar Source